search-fail-pivot

name	search-fail-pivot
description	Heuristic for detecting search tool failures and pivoting to domain knowledge for document generation

Search Tool Failure Detection and Pivot Strategy

Purpose

When using search_web or similar external data retrieval tools, repeated failures waste iterations. This skill provides a diagnostic heuristic to recognize when to abandon search attempts and proceed with existing domain knowledge.

Core Heuristic

If search_web fails 2+ consecutive times with 'unknown error' or similar non-recoverable errors, pivot to domain knowledge + run_shell for document generation.

Do not attempt more than 2 retries on the same or similar queries before pivoting.

Step-by-Step Instructions

Step 1: Track Search Failures

Monitor search_web outcomes during your task:

• Count consecutive failures (empty results, 'unknown error', timeout, access denied)
• Note the error type - distinguish between:
  • Recoverable: Rate limiting, temporary timeout (retry 1-2 times)
  • Non-recoverable: 'unknown error', persistent empty results, access denied

Step 2: Apply the 2-Failure Rule

IF search_web failures >= 2 (same or similar queries)
THEN:
  1. Stop attempting search_web
  2. Document what information you attempted to retrieve
  3. Proceed with existing domain knowledge

Step 3: Pivot to Domain Knowledge

When pivoting:

1. Acknowledge the limitation: Note that external verification was attempted but unavailable
2. Use established knowledge: Draw on training data for well-known facts, laws, cases, standards
3. Generate with run_shell: Create documents using Python/bash scripts rather than waiting for external data

Example pivot workflow:

# Instead of continuing search_web attempts:
# 1. Compile known information from domain knowledge
known_facts = {
    "law": "COPPA requirements for children's data",
    "precedent": "FTC v. Google/YouTube settlement patterns",
    "jurisdiction": "California privacy law framework"
}

# 2. Generate document directly
# Use run_shell with Python to create PDF/DOC

Step 4: Document Generation Pattern

When generating documents after pivoting:

# Use run_shell to execute document generation
# Example: Create legal memo with Python + reportlab
python << 'EOF'
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas

# Generate document with domain knowledge
c = canvas.Canvas("memo.pdf", pagesize=letter)
c.drawString(100, 750, "Legal Analysis based on established precedents")
# ... continue with known information
c.save()
EOF

When This Applies

This heuristic is particularly useful for:

Task Type	Why It Applies
Legal research	Case law and statutes are well-documented in training
Technical documentation	Standards and best practices are established knowledge
Historical analysis	Past events and data are in training corpus
Regulatory compliance	Major regulations (GDPR, COPPA, HIPAA) are well-known

When NOT to Pivot

Do NOT apply this heuristic when:

• You need current/time-sensitive data (stock prices, weather, news < 24hrs)
• The task explicitly requires external verification
• You have not yet attempted the search (first attempt should complete)
• Errors are clearly recoverable (rate limit with retry-after header)

Example Decision Flow

Attempt search_web → Success? → Yes → Use results
                         ↓
                        No
                         ↓
            Is this failure #1? → Yes → Retry once with modified query
                         ↓
                        No (failure #2+)
                         ↓
            Pivot to domain knowledge + run_shell document generation

Benefits

• Saves iterations: Avoids wasting 3-4 attempts on unavailable data
• Maintains progress: Task continues instead of stalling on search
• Leverages training: Uses the substantial knowledge already available
• Clear handoff: Explicitly documents the pivot decision for transparency

Anti-Patterns to Avoid

❌ Continuing search_web attempts beyond 2 failures
❌ Not documenting why you pivoted from search
❌ Applying this to tasks requiring real-time data
❌ Abandoning search on first failure without retry

✅ Apply 2-failure rule consistently
✅ Document the pivot decision in your output
✅ Use run_shell efficiently after pivoting
✅ Distinguish recoverable vs non-recoverable errors